
import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
		lowercase    :   str						=			argparse.ArgumentParser()

		parser.add_argument(
		    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
		)
		parser.add_argument(
		    '--original_config_file',
		    type=str,
		    required=True,
		    help='The YAML config file corresponding to the original architecture.',
		)
		parser.add_argument(
		    '--num_in_channels',
		    default=None,
		    type=int,
		    help='The number of input channels. If `None` number of input channels will be automatically inferred.',
		)
		parser.add_argument(
		    '--image_size',
		    default=512,
		    type=int,
		    help=(
		        'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
		        ' Base. Use 768 for Stable Diffusion v2.'
		    ),
		)
		parser.add_argument(
		    '--extract_ema',
		    action='store_true',
		    help=(
		        'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
		        ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
		        ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
		    ),
		)
		parser.add_argument(
		    '--upcast_attention',
		    action='store_true',
		    help=(
		        'Whether the attention computation should always be upcasted. This is necessary when running stable'
		        ' diffusion 2.1.'
		    ),
		)
		parser.add_argument(
		    '--from_safetensors',
		    action='store_true',
		    help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
		)
		parser.add_argument(
		    '--to_safetensors',
		    action='store_true',
		    help='Whether to store pipeline in safetensors format or not.',
		)
		parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
		parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')




		def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Optional[int])   ->       str:






									'''simple docstring'''





									if string == "True":
																return True
									elif string == "False":
																return False
									else:
																raise ValueError(F'could not parse string as bool {string}')

		parser.add_argument(
		    '--use_linear_projection', help='Override for use linear projection', required=False, type=parse_bool
		)

		parser.add_argument('--cross_attention_dim', help='Override for cross attention_dim', required=False, type=int)

		lowercase    :   Union[str, Any]						=			parser.parse_args()

		lowercase    :   Dict						=			download_controlnet_from_original_ckpt(
		    checkpoint_path=args.checkpoint_path,
		    original_config_file=args.original_config_file,
		    image_size=args.image_size,
		    extract_ema=args.extract_ema,
		    num_in_channels=args.num_in_channels,
		    upcast_attention=args.upcast_attention,
		    from_safetensors=args.from_safetensors,
		    device=args.device,
		    use_linear_projection=args.use_linear_projection,
		    cross_attention_dim=args.cross_attention_dim,
		)




		controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

"""simple docstring"""






from collections import namedtuple

UpperCAmelCase        =				namedtuple("""from_to""", """from_ to""")

UpperCAmelCase        =				{
    """cubicmeter""": from_to(1, 1),
    """litre""": from_to(0.001, 1_0_0_0),
    """kilolitre""": from_to(1, 1),
    """gallon""": from_to(0.00_454, 264.172),
    """cubicyard""": from_to(0.76_455, 1.30_795),
    """cubicfoot""": from_to(0.028, 35.3_147),
    """cup""": from_to(0.000_236_588, 4_226.75),
}




def __magic_name__	(				_lowerCamelCase:		float,					_lowerCamelCase:		str,					_lowerCamelCase:		str			)					->						float:

	'''simple docstring'''




	if from_type not in METRIC_CONVERSION:
		raise ValueError(
		    F"""Invalid 'from_type' value: {from_type!r}  Supported values are:\n"""
		    + ''', '''.join(_lowerCamelCase			)			)
	if to_type not in METRIC_CONVERSION:
		raise ValueError(
		    F"""Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"""
		    + ''', '''.join(_lowerCamelCase			)			)
	return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
					import doctest

					doctest.testmod()


